Techniques for determining the effects on a system of a component that has four states

ABSTRACT

Techniques for determining effects on a biological system include determining rate constants for a particular time interval starting at an initial time. Each rate constant indicates a rate of transition from one of four states to a different one of the four states for a component of a biological system in presence of an external factor. A temporal change in a probability that the component is in a particular state after the initial time is determined without numerical iteration over multiple time steps. This includes determining three relaxation time constants that describe exponential changes based on the rate constants. The effect of the external factor on the biological system is determined based on the temporal change in the probability that the component is in the particular state. The probability at an arbitrary time is determined based on the rate constants and initial probabilities.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a continuation of and claims benefit ofapplication Ser. No. 11/687,635, filed Mar. 17, 2007, which claimsbenefit of Provisional Appln. 60/783,935, filed Mar. 20, 2006, theentire contents of which are hereby incorporated by reference as iffully set forth herein, under 35 U.S.C. §119(e).

STATEMENT OF GOVERNMENTAL INTEREST

This invention was made with Government support under Contract No.HL068733 awarded by the National Institutes of Health. The Governmenthas certain rights in the invention.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention is directed to determining an effect of a factoron a biological system by determining a temporal change in probabilityof a component occupying a particular one of up to four distinguishableconfigurations, called states. In a particular embodiment, an effect ofa factor (such as presence of a ligand or introduction of a voltagechange) on an ion channel in a membrane of a cell is determined bydetermining a temporal change in probability that the ion channel is inone of four distinguishable states.

2. Description of the Related Art

Many biological systems important to a normal or diseased processes in acell can be described as dependent on the population of different statesof each of one or more different components, especially proteinmolecules. A protein molecule typically has many possibleconfigurations, both absent external forcing and in response to thepresence of external factors that exert some force on the protein.External factors can take many forms, from the application of anexternal voltage to the presence of ligands that bind at one or moresites on the protein. The probability that a protein is in oneconfiguration or another is affected by these factors. The probabilitycan be expressed by repeating an experiment many times with the sameprotein starting configuration and the introduction of the same factorand counting the occurrence of each state in the group of experiments.By the same token, the probability can be expressed by determining thepercentage of a large number of the same protein that occupies each ofthe states. The bulk process of the cell is then dependent in part onthe percentage of the population in each of the states.

For example, a protein that resides in a cell interior is acted upon bya therapeutic pharmacological agent when the protein is in oneconfiguration, but not in a different configuration. The effectivenessof the therapeutic agent then depends on the probability that theprotein is in a particular state, which corresponds to the population ofthe different states for a large number of molecules of that protein.

In one well known and heavily studied example, the protein moleculeserves as a gated ion channel in a cell membrane that blocks passage ofa certain ion in one or more states and allows passage of the certainions through the membrane in one or more other states that can beinduced by application of a certain voltage as an external gatingfactor. The controlled passage of such ions is essential in some veryimportant bulk cell processes, such as the conduction of electricalimpulses in nerve cells and muscle cells. The open state of an ionchannel can be measured by a current that flows through the channel.When a channel is closed or inactive, no ions flow and current is zero.When an ion channel is inactive, it does not respond to the externalgating factor by switching to an open state.

FIG. 1 is graph 100 that illustrates the measured responses of an ionchannel to a voltage change. Graph 100 includes a trace 110 of voltagewith time, eight individual traces 120 a, 120 b, 120 c, 120 d, 120 e,120 f, 120 g, 120 h (collectively referenced herein as individual traces120) of ion current with time for a single ion channel, and an averagetrace 130 of ion current with time for 40 consecutive single channeltraces. FIG. 1 is from L. Goldman, “Stationarity of Sodium ChannelGating Kinetics in Excised Patches from Neuroblastoma N1E 115,”Biophysical Journal, vol. 69, pp 2364 to 2368, December 1995(hereinafter Goldman I), the entire contents of which are herbyincorporated by reference as if fully set forth herein. A horizontalaxis scale 102 of graph 100 indicates an amount of time associated withthe scale distance along a horizontal direction for trace 110 and traces120 in graph 100. The length of scale 102 corresponds to 4 milliseconds(ms, 1 ms=10⁻³ seconds). A horizontal axis scale 103 of graph 100indicates an amount of time associated with the scale distance along ahorizontal direction for trace 130. The length of scale 103 correspondsto 5 ms. The vertical change in voltage for trace 110 corresponds to avoltage step from −120 milliVolts (mV, 1 mV=10⁻³ Volts) to −30 mV, whichis held for 45 ms. A current is measured for a limited time after thevoltage change in 7 traces (trace 120 a, trace 120 b, trace 120 d, trace120 e, trace 120 f, trace 120 g, trace 120 h) of the eight traces 120,as indicated by current in a negative direction through the membrane attrace portions 125 a, 125 b, 125 d, 125 e, 125 e, 125 f, 125 g,corresponding to the 7 traces, respectively. Such a current indicatesthe single ion channel is in an open state. The current associated withan open state in traces 120 is indicated by the vertical scale 104 thatindicates a current of 3 picoAmperes (pA, 1 pA=10⁻² Amperes). As can beseen from traces 120, the ion channel under study in FIG. 1 is moreprobably in the open state shortly after the voltage change, and then ismore probably in a non-open state after about 20 ms. A more stablemeasurement of current is obtained by averaging over 40 such measurementto produce average trace 130. The current associated with average trace130 is smaller than the individual measured currents, as indicated bythe vertical scale 106 for trace 130, which corresponds to only 0.4 pA.

The features of average trace 130 include an exponentially increasingcurrent in a negative direction along portion 131, and an exponentiallydecreasing current in the negative direction along portions 132 and 133.The response of such proteins to the presence or absence of externalforcing is the object of efforts to predict and control cell processes,for both diagnosis and treatment of disease. Example uses include drugresponse and drug screening applications. The features of average trace130 not only represent measured current changes but also indicate thetemporal change in the probability that the ion channel is in the openstate of the three or four states used to explain the membrane behavior.Viewed in this light, FIG. 1 shows an exponentially increasingprobability of an open state along portion 131, and an exponentiallydecreasing probability of the open state along portions 132 and 133.

Observations of the type depicted in FIG. 1 have been explained byproposing three states for the protein serving as the ion channel. Otherobservations showing a delay in response to a voltage change have beenexplained using four or more states, including two or more closed orinactivation states (e.g., see R. Hahin and L. Goldman, “InitialConditions and the Kinetics of the Sodium Conductance in Myxicola GiantAxons, I. Effects on the Time-Course of the Sodium Conductance,” Journalof General Physiology, v72, pp 863-877, 1978;. Goldman, “Gating currentkinetics in Myxicola giant axons,” Biophysical Journal, v59, pp 574-589,1991; and L. Goldman, “Sodium Channel Inactivation from Closed States:Evidence for an Intrinsic Voltage Dependency,” Biophysical Journal, v69,pp 2369-2377, 1995, the entire contents of each of which are herebyincorporated by reference as if fully set forth herein). Of course, eachstate used to explain an observable behavioral effect may consist of oneor more actual states.

In many circumstances, the temporal changes of probability for statesfor a particular protein are predicted based on an initial populationdistribution among the states, values for rate constants for thetransition from one state to another, and a system of ordinarydifferential equations. Generally, such rate constants can be assumedconstant only for a particular protein and fixed external forcing.Nevertheless, much progress is made in predicting cell processes underthese conditions. The solutions require numerical computations that areeffectively performed only using computers, iterative methods, and longiteration times with many time steps.

The process is used both to determine rate constants that matchobservations in the presence of various factors, e.g., to fit the rateconstants to the observations, and also to predict the behavior of thebiological system based on the rate constants determined independentlyof those observations, e.g., based on previously determined rateconstants in earlier experiments in the same laboratory or published inthe scientific literature.

Analytical solutions that do not require computationally intenseiterative methods have been developed for fewer than four states and forfour states when certain rate constants are eliminated (forced to zero).However, with no general analytical solution, many workers are forced toemploy the computationally intense iterative methods over many smalltime steps to deduce the temporal changes in probability for aparticular state or states. This impedes both the determination of rateconstants to match data and the prediction of effects of an externalfactor on a system, given its effect on one or more rate constants.

Based on the foregoing, there is a clear need for an analytical solutionfor time changes in probability for a general four state biologicalsystem based on rate constants alone or in combination with initialpopulation distributions of the four states.

SUMMARY OF THE INVENTION

Techniques are provided for determining effects on a system of acomponent with four states. The techniques include determining rateconstants for a particular time interval starting at an initial time.Each rate constant indicates a rate of transition from one of fourstates to a different one of the four states for a component of abiological system in presence of an external factor. A temporal changein a probability that the component is in a particular state after theinitial time is determined without numerical iteration over multipletime steps. This includes determining three relaxation time constantsthat describe exponential changes based on the rate constants. Theeffect of the external factor on the biological system is determinedbased on the temporal change in the probability that the component is inthe particular state.

In some embodiments initial probabilities for the four states are alsodetermined. Then the probability at an arbitrary time is determinedbased on the rate constants and the initial probabilities.

These techniques allow temporal changes in behavior of system to be morequickly predicted. In turn, this allows predictions to be more easilycompared to observations, and rate constants to be more quickly andeasily tuned to observations.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention is illustrated by way of example, and not by wayof limitation, in the figures of the accompanying drawings and in whichlike reference numerals refer to similar elements and in which:

FIG. 1 is graph that illustrates the measured responses of an ionchannel to a voltage change;

FIG. 2A is a flow diagram that illustrates at a high level a method fordetermining the effect on a biological system of a factor that affectsany transition between two of four states of a component of that system,according to an embodiment;

FIG. 2B is a flow diagram that illustrates in more detail a step of themethod of FIG. 2A, according to an embodiment;

FIG. 3 is a block diagram that illustrates the four states and 12associated rates for transitions between states, according to an exampleembodiment; and

FIG. 4 is a block diagram that illustrates a computer system upon whichan embodiment of the invention may be implemented; and

DETAILED DESCRIPTION

A method, computer-readable medium and apparatus are described fordetermining an effect of an external factor on a biological system. Inthe following description, for the purposes of explanation, numerousspecific details are set forth in order to provide a thoroughunderstanding of the present invention. It will be apparent, however, toone skilled in the art that the present invention may be practicedwithout these specific details. In other instances, well-knownstructures and devices are shown in block diagram form in order to avoidunnecessarily obscuring the present invention.

Some embodiments of the invention are described below in the context ofan ion channel in a cell membrane in which the ion channel occupies oneof an open state, a first closed state, a second close statecorresponding to a delayed response to an external voltage, and aninactive state that does not open in response to an external voltage,but may recover to one of the closed states in response to an externalvoltage. One of these embodiments is described by L. Goldman,“Quantitative Analysis of a Fully Generalized Four State Genetic Scheme,”Biophysical Journal, vol. 91, pp 173-178, July 2006 (hereinafterGoldman), the entire contents of which are hereby incorporated byreference as if fully set forth herein.

However, the invention is not limited to this context. In otherembodiments, other biological systems, such as other cell organelles,are involved, having four or fewer states of proteins or othercomponents of the biological system, that respond to other physical orchemical factors. For example, in some embodiments an ion channel in acell membrane responds to a concentration of a ligand or other moleculein the vicinity of the ion channel. In some embodiments, the componentis a protein that is not an ion channel for a cell membrane, and theexternal factor is a chemical compound of a class called a ligand, areceptor agonist, a receptor antagonist, an antibody, and a therapeuticagent. In still other embodiments, other non-biological systems, such aschemical, physical, climatic systems, are modeled with four states.

Furthermore, embodiments are described in the context of a forwardanalytical computation, starting with rate constants with or withoutinitial probabilities and deducing temporal changes in probability, suchas one or more relaxation time constants, of one or more states.However, in other embodiments, the reverse computation can also beperformed analytically, without resort to numerical iterative methods,by making some simplifying assumptions. For example, given observedvalues of one or more relaxation time constants or steady stateprobabilities based on observed temporal changes, a corresponding numberof unknown values of rate constants can be deduced by using algebra toinvert one or more of the equations presented. Once inverted, theinverted equations can be used repeatedly, to deduce those rateconstants from the relaxation times or steady state probabilities.Similarly, observations of probability amplitudes for one or more statesat one or more times before steady state can be used to deduce acorresponding number of initial probabilities.

1. OVERVIEW

FIG. 2A is a flow diagram that illustrates at a high level a method 200for determining the effect on a biological system of a factor thataffects any transition between two of four states of a component of thatsystem, according to an embodiment. Although steps are shown in FIG. 2Aand subsequent flow diagrams in a particular order for purposes ofillustration, in other embodiments one or more steps may be performed ina different order or overlapping in time or may be omitted or changed insome combination of ways.

In step 210 data is received that indicates rate constants fortransitions among four states for a component of a biological system.Any method known in the art for determining and receiving the rateconstants data may be used. In some embodiments, the rate constants areentered manually or retrieved from a local or remote data base, or sentin response to a request sent to a different process on the same ordifferent local or remote computer or received unsolicited from aprocess on the same or different local or remote computer. In someembodiments, the rate constants are based on values in publishedscientific literature. In some embodiments, rate constants are based onprevious experiments. In some embodiments, one or more rate constantsare based on estimated values refined based on output from a previousexecution of the steps of method 200.

The four states for which rate constants are received are designatedherein by the numerals 0, 1, 2, 3. A rate constant is designated by theletter k followed by two numerals, the first indicating the source stateand the second indicating the destination state. There are twelve rateconstants to be received during step 210 to encompass all transitionsamong the four states: k01, k10, k02, k20, k03, k30, k12, k21, k13, k31,k23, k32. FIG. 3 is a block diagram that illustrates the four states331, 332, 333, 334 and 12 associated rates 301, 302, 303, 304, 305, 306,307, 308, 309, 310, 311, 312 for transitions 300 between states,according to an example embodiment.

The rate constants received during step 210 apply in the presence ofcertain values for one or more external factors that affect thebiological system that are introduced at a certain time, called theinitial time. The rate constants are assumed constant so long as theexternal factor values are approximately constant.

In step 220, data is received that indicates initial probabilities forthe four states. Any method known in the art for receiving the initialprobability data may be used, as described above for rate constantsdata. A probability is designated herein by the letter P followed by anumeral that indicates the state. A probability of a state at theinitial time is called the initial probability and is designated by theletter “i” following the numeral that indicates the state. There are upto four initial probabilities to be received during step 220 toencompass all four states: P0 i, P1 i, P2 i, P3 i. In some embodiments,the initial probabilities are determined based on the steady stateprobabilities computed in a previous execution of method 200 fordifferent rate constants. The computation of steady state probabilities(designated: P0∞, P1∞, P2∞, P3∞ for states 0, 1, 2, 3, respectively,herein) are described in more detail below with reference to FIG. 2B. Insome embodiments, in which only exponential relaxation times or steadystate probabilities, or both, are determined, as described in moredetail below with respect to FIG. 2B, step 220 is omitted.

In step 240, temporal changes in probability for one or more states aredetermined without numerical iterations of time differentials overmultiple time steps. This direct determination without computationallyintensive numerical iterations provides a distinct advantage for method200 over prior art approaches when four states are involved. Step 240 isdescribed in more detail below with reference to FIG. 2B.

In some embodiments, one or more of three relaxation time constants aredetermined for the change in probability of one or more states. Thethree relaxation time constants for state 0 are designated herein as“a,” “b,” “c.” Three relaxation time constants are a characteristic of afour state system and can explain different exponential changes inprobability with time in different portions of a trace (for example inportions 131, 132 and 133 of trace 130). Unlike prior approaches, thethree relaxation time constants can be computed directly from rates,without numerical iteration and curve fitting. In some embodiments, oneor more of four probabilities at one or more arbitrary times “t” aredetermined for the change in probability of corresponding one or morestates. The four probabilities corresponding to the four states at time“t” are designated herein as “P0(t),” “P1(t),” “P2(t)” and “P3(t),”respectively. In some embodiments, step 240 includes presenting datathat indicates the predicted change in probability of one or morestates, e.g., a, b, c, P0∞, P1∞, P2∞, P3∞ or P0(t), or some combination.

In step 280, an effect on a biological system is determined based on thetemporal change in probability. For example, a temporal change inaverage ion current is determined based on the temporal change inprobability of an open state for an ion channel. In some embodiments,step 280 includes presenting data that indicates the predicted behaviorof the biological system

In step 290, the effect on the biological system is compared tomeasurements of the biological system. For example, a prediction of ioncurrent determined in step 280 is compared to trace 130 in FIG. 1. Insome embodiments, step 290 is omitted. For example, in drug screening orexperimental planning, a variety of effects are determined in step 280to determine which drug to test or experiment to perform before anymeasurements are obtained. In some embodiments, step 290 includespresenting data that indicates the relation of predicted and measuredbehavior of the biological system.

In step 295, it is determined whether to adjust rate constants orinitial probabilities or both. For example, in embodiments in which therate constants are not known precisely, initial guesses for one or morerate constants are used in step 210. Based on agreement withmeasurements in step 290, the rate constants are accepted or changed.Any method may be used to determine whether to adjust the rate constantsand initial probabilities, and by how much. For example, optimalestimation techniques well known in the art may be used to determine agoodness of fit, and a change of goodness of fit with each change in oneor more rate constants, and a next guess for one or more rate constants.In some embodiments, the changes in one or more rate constants aredetermined analytically by inverting one or more algebraic equationsused during step 240 and making some simplifying assumptions, asappropriate. In some embodiments, step 295 is omitted and control passesdirectly to step 299 to terminate method 200.

If it is determined in step 295 to adjust one or more rate constants orinitial probabilities, then control passes back to step 210 to receivedata indicating the new rate constants, if any, and to step 220 toreceive the new initial conditions, if any.

If it is determined in step 295 not to adjust one or more rate constantsor initial probabilities, then control passes to step 299.

2. Example Embodiments

FIG. 2B is a flow diagram that illustrates in more detail step 240 ofthe method of FIG. 2A, according to embodiment 241. The steps ofembodiment 241 are described below in detail for the four states of anion channel, and are described in Goldman.

The four states for an ion channel are defined in Goldman as Open,Closed 1, Closed 2, and Inactive represented by the symbols O, 1, 2, IN,respectively. One of the closed 1 and closed 2 states represents a statein which there is a perceptibly longer delay between onset of a voltagechange and an opening of an ion channel, the other closed staterepresents a closed state in which there is a shorter delay. Forpurposes of illustration, it is assumed that the ion channel states O,1, 2, IN correspond to the general four states 0, 1, 2, 3, respectively.Furthermore, in Goldman, the rate constants are also designated by theletter k, but with the source and destination states expressed assubscripts following the letter k. The association between symbols usedin Goldman and those used in the general case are listed in Table 1.

TABLE 1 Mapping between symbols used herein and those used in Goldman.property general symbol(s) Symbol(s) in Goldman states 0, 1, 2, 3 O, 1,2, IN rate constants k01, k10, . . . k_(O1), k_(1O), . . . (k)Combinations K1 through K12 K₁ through K₁₂ of k Probability P0 Y ofstate 0 Probability P1 X of state 1 Probability P2 W of state 2Probability P3 Z of state 3 Initial P0i, P1i, P2i, P3i Y(0), X(0), W(0),Z(0) Probability of states Initial rates of temporal change P0i′, P1i′,P2i′, P3i′${\frac{d\; Y}{d\; t}(0)},{\frac{d\; X}{d\; t}(0)},{\frac{d\; W}{d\; t}(0)},{\frac{d\; Z}{d\; t}(0)}$Initial rates of temporal curvature P0i″, P1i″, P2i″, P3i″${\frac{d^{2}Y}{d\; t^{2}}(0)},{\frac{d^{2}X}{d\; t^{2}}(0)},{\frac{d^{2}W}{d\; t^{2}}(0)},{\frac{d^{2}Z}{d\; t^{2}}(0)}$Steady state P0∞, P1∞, P2∞, P3∞ Y(∞), X(∞), W(∞), Z(∞) probabilities

In step 242, three exponential relaxation time constants are determinedbased on the rate constants received in step 210, without numericaliteration over multiple time steps. For example, the relaxation timeconstants a, b, c for the Open state are computed based only on the rateconstants by evaluating equations provided in Goldman. These constantsare evaluated starting with Goldman equations 9 through 20 that definetwelve combinations K1, K2, K3, K4, K5, K6, K7, K8, K9, K10, K11, K12 ofthe rate constants. Then Goldman equations 22 through 25 that defineterms α, β, γ, ε are evaluated. Then Goldman equations 43 and 44 thatdefine terms A and B are evaluated. Then Goldman equations 41 and 42that define terms θ and φ are evaluated. Then Goldman equations 38through 40 that define relaxation constants a, b, c are evaluated. Therelevant Goldman Equations are provided here as Equations 1 through 23.K1=k12+k10+k13+k21  (1)K2=k01−k21  (2)K3=k31−k21  (3)K4=k21  (4)K5=k01+k02+k03+k20  (5)K6=k10−k20  (6)K7=k30−k20  (7)K8=k20  (8)K9=k32+k31+k30+k23  (9)K10=k13−k23  (10)K11=k03−k23  (11)K12=k23  (12)α=K1+K5+K9  (13)β=K1*K9+K1*K5+K5*K9−(K2*K6+K3*K10+K7*K11)  (14)γ=K1*K5*K9−(K1*K7*K11+K2*K6*K9+K2*K7*K10+K3*K6*K11+K3*K5*K10)  (15)δ=K3*K8*K10−(K1*K7*K12+K1*K8*K9+K4*K7*K10+K4*K6*K9+K3*K6*K12)  (16)A=β−(α²/3)  (17)B=−1/27(2α³−9 α*β+27γ)  (18)θ=³ √[−B/2+√(B ²/4+A ³/27)]  (19)φ=³ √[−B/2−√(B ²/4+A ³/27)]  (20)a=α/3+(θ+φ)  (21)b=α/3−[(θ+φ)/2+((θ−φ)/2)*√(−3)]  (22)c=α/3−[(θ+φ)/2−((θ−φ)/2)*√(−3)]  (23)

These time constants can be used to describe the exponential changes inprobability of a state over time using the mathematical constant base ofthe natural logarithm, designated “e,” according to the form P0(t) isproportional to e^(−at) or e^(−bt) or e^(−ct) or some combination, in atleast a portion of a probability trace. In some embodiments, no morecomputations are made during step 240 and control returns to step 280after step 242. In some of these embodiments, fewer than all three ofEquations 21 through 23 are evaluated, to produce fewer than all threerelaxation time constants a, b, c. In such embodiments, step 220 toreceive the initial probabilities P0 i, P1 i, P2 i, P3 i, may beomitted.

An advantage of the analytical solution for relaxation time constants a,b, c is that an analytical equation relates a given relaxation timeconstant to a value of a particular rate constant or subset of rateconstants. This is not directly known using the iterative numericalmethods. With the iterative numerical methods, each kij value exploredwould require another P0(t) simulation and then a time constants deducedby curve fitting each simulated trace. Often, just one or more simulatedtraces are presented to a user to assess visually—a tedious and errorprone task. To determine whether other factors have affected theobservations, additional iterative numerical simulations would have tobe run. Third order differential equations would have to be derived andthe relationship between the rate constants and α, β, γ would have to bedefined.

In step 244 one or more steady state probabilities (designated “P0∞,”“P1∞,” “P2∞,” “P3∞” for states 0 through 3, respectively) are determinedfor a corresponding one or more states, without numerical iteration overmultiple time steps. The steady state probabilities are theprobabilities at steady state when further temporal changes inprobability are negligible. For example, the steady state probabilitiesP0∞, P1∞, P2∞, P3∞ are computed based only on the rate constants byfurther evaluating additional equations in Goldman, beyond thoseevaluated for step 242. These constants are evaluated with Goldmanequations 27 through 31 (in which the steady state probabilities P0∞,P1∞, P2∞, P3∞ are represented by the symbols Y(∞), X(∞), W(∞), Z(∞),respectively, as listed in Table 1). In some embodiments fewer than allsteady state probabilities are evaluated. For example, in someembodiments only P0∞ is evaluated using Equation 24. The relevantGoldman equations are provided here as Equations 24 through 27.P0∞=(K1*K7*K12+K1*K8*K9+K4*K7*K10+K4*K6*K9+K3*K6*K12−K3*K8*K10)/γ  (24)P1∞=(K2*K7*K12+K3*K5*K12+K2*K8*K9+K4*K5*K9+K3*K8*K11−K4*K7*K11)/γ  (25)P3∞=(K1*K8*K11+K1*K5*K12+K2*K8*K10+K4*K5*K10+K4*K6*K11−K2*K6*K12)/γ  (26)P2∞=1−P0∞−P1∞−P3∞  (27)

In some embodiments, no more computations are made during step 240 andcontrol returns to step 280 after step 244. In such embodiments, step220 to receive the initial probabilities P0 i, P1 i, P2 i, P3 i, may beomitted. Again, an advantage of the analytical solution for relaxationtime constants steady state probabilities is that an analytical equationrelates a given steady state probability to a value of a particular rateconstant or subset of rate constants. This is not directly known usingthe iterative numerical methods.

In step 250 initial rates of temporal change for at least three of thefour states (designated P0 i′, P1 i′, P2 i′, P3 i′ for states 0 through3, respectively) are determined, without numerical iteration overmultiple time steps. The initial rates of temporal change are the firsttime derivative of the probabilities at the initial time (t=0). Forexample, initial rates of temporal change P0 i′, P1 i′, P3 i′ arecomputed based on the rate constants and the initial probabilities P0 i,P1 i, P3 3 by further evaluating additional equations in Goldman, beyondthose evaluated for step 242 and 244. These constants are evaluated withGoldman equations 6 through 8 at time t=0, substituting P0 i, P1 i, P3 3for Y, X and Z, respectively. In embodiments that include step 250, step220 to receive the initial probabilities P0 i, P1 i, P2 i, P3 i, shouldbe included. The relevant Goldman equations are provided here asEquations 28 through 30.P0i′=−K5*P0i+K6*P1i+K7*P3i+K8  (28)P1i′=−K1*P1i+K2*P0i+K3*P3i+K4  (29)P3i′=−K9*P3i+K10*P1i+K11*P0i+K12  (30)

In step 252, initial rates of temporal curvature for at least one of thefour states (designated P0 i″, P1 i″, P2 i″, P3 i″ for states 0 through3, respectively) is determined, without numerical iteration overmultiple time steps. The initial rates of temporal curvature are thesecond time derivative of the probabilities at the initial time (t=0).For example, initial rate of temporal curvature P0 i″ is computed basedon the rate constants and the initial probabilities P0 i, P1 i, P3 3 byfurther evaluating additional equations in Goldman, beyond thoseevaluated for step 242, 244 and 250. These constants are evaluated withGoldman equation 37, substituting P0 i″, P0 i′, P1 i′, P3 i′ ford²Y/dt², dY/dt, dX/dt and dZ/dt, respectively. In embodiment thatinclude step 252, step 220 to receive the initial probabilities P0 i, P1i, P2 i, P3 i, should be included. The relevant Goldman equation isprovided here as Equations 31.P0i″=−K5*P0i′+K6*P1i′+K7*P3i′  (31)

In step 260 probability of one or more states at an arbitrary time t(designated P0(t), P1(t), P2(t), P3(t), for states 0 through 3,respectively) is determined, without numerical iteration over multipletime steps. For example, P0(t) is computed using the time t and thevalues determined above based on rate constants and the initialprobabilities P0 i, P1 i, P3 3 by further evaluating an additionalequation in Goldman, beyond those evaluated for step 242, 244, 250 and252. P0(t) is computed with Goldman equation 36, substituting P0 i″, P0i′, P0 i, and P0∞ for d²Y/dt², dY/dt, Y(0) and Y(∞), respectively.Goldman equation 36 is equivalent to the following Equations 32 to 35for general states 0, 1, 2, 3:Ca=[P0i″+(b+c)*P0i′+b*c*(P0i−P0∞)]/[(a−c)*(b−a)]  (32)Cb=[P0i″+(a+c)*P0i′+a*c*(P0i−P0∞)]/[(c−b)*(b−a)]  (33)Cc=[P0i″+(a+b)*P0i′+a*b*(P0i−P0∞)]/[(c−b)*(a−c)]  (34)P0(t)=P0∞−Ca*e ^(−at) −Cb*e ^(−bt) −Cc*e ^(−ct)  (35)

It is noted that for the duration of a time interval after the initialtime during which the external factors are constant, the rate constantsand the initial probabilities are also constant. As a result, the termscomputed during steps 242 through 252 are also constant, i.e., do notvary with t over the time interval. That is the terms P0∞, Ca, Cb, Cc,a, b, c and e in the final equation are all constant. Thus steps 242through 252 are executed once, then step 260 can be executed as manytimes as desired for a corresponding number of times t, in any order andwith any large temporal separation. Such is not the case with prior artapproaches that use numerical iterative techniques to advance theprobabilities in small temporal increments.

3. Hardware Overview

FIG. 4 is a block diagram that illustrates a computer system 400 uponwhich an embodiment of the invention may be implemented. Computer system400 includes a communication mechanism such as a bus 410 for passinginformation between other internal and external components of thecomputer system 400. Information is represented as physical signals of ameasurable phenomenon, typically electric voltages, but including, inother embodiments, such phenomena as magnetic, electromagnetic,pressure, chemical, molecular atomic and quantum interactions. Forexample, north and south magnetic fields, or a zero and non-zeroelectric voltage, represent two states (0, 1) of a binary digit (bit). Asequence of binary digits constitutes digital data that is used torepresent a number or code for a character. A bus 410 includes manyparallel conductors of information so that information is transferredquickly among devices coupled to the bus 410. One or more processors 402for processing information are coupled with the bus 410. A processor 402performs a set of operations on information. The set of operationsinclude bringing information in from the bus 410 and placing informationon the bus 410. The set of operations also typically include comparingtwo or more units of information, shifting positions of units ofinformation, and combining two or more units of information, such as byaddition or multiplication. A sequence of operations to be executed bythe processor 402 constitute computer instructions.

Computer system 400 also includes a memory 404 coupled to bus 410. Thememory 404, such as a random access memory (RAM) or other dynamicstorage device, stores information including computer instructions.Dynamic memory allows information stored therein to be changed by thecomputer system 400. RAM allows a unit of information stored at alocation called a memory address to be stored and retrievedindependently of information at neighboring addresses. The memory 404 isalso used by the processor 402 to store temporary values duringexecution of computer instructions. The computer system 400 alsoincludes a read only memory (ROM) 406 or other static storage devicecoupled to the bus 410 for storing static information, includinginstructions, that is not changed by the computer system 400. Alsocoupled to bus 410 is a non-volatile (persistent) storage device 408,such as a magnetic disk or optical disk, for storing information,including instructions, that persists even when the computer system 400is turned off or otherwise loses power.

Information, including instructions, is provided to the bus 410 for useby the processor from an external input device 412, such as a keyboardcontaining alphanumeric keys operated by a human user, or a sensor. Asensor detects conditions in its vicinity and transforms thosedetections into signals compatible with the signals used to representinformation in computer system 400. Other external devices coupled tobus 410, used primarily for interacting with humans, include a displaydevice 414, such as a cathode ray tube (CRT) or a liquid crystal display(LCD), for presenting images, and a pointing device 416, such as a mouseor a trackball or cursor direction keys, for controlling a position of asmall cursor image presented on the display 414 and issuing commandsassociated with graphical elements presented on the display 414.

In the illustrated embodiment, special purpose hardware, such as anapplication specific integrated circuit (IC) 420, is coupled to bus 410.The special purpose hardware is configured to perform operations notperformed by processor 402 quickly enough for special purposes. Examplesof application specific ICs include graphics accelerator cards forgenerating images for display 414, cryptographic boards for encryptingand decrypting messages sent over a network, speech recognition, andinterfaces to special external devices, such as robotic arms and medicalscanning equipment that repeatedly perform some complex sequence ofoperations that are more efficiently implemented in hardware.

Computer system 400 also includes one or more instances of acommunications interface 470 coupled to bus 410. Communication interface470 provides a two-way communication coupling to a variety of externaldevices that operate with their own processors, such as printers,scanners and external disks. In general the coupling is with a networklink 478 that is connected to a local network 480 to which a variety ofexternal devices with their own processors are connected. For example,communication interface 470 may be a parallel port or a serial port or auniversal serial bus (USB) port on a personal computer. In someembodiments, communications interface 470 is an integrated servicesdigital network (ISDN) card or a digital subscriber line (DSL) card or atelephone modem that provides an information communication connection toa corresponding type of telephone line. In some embodiments, acommunication interface 470 is a cable modem that converts signals onbus 410 into signals for a communication connection over a coaxial cableor into optical signals for a communication connection over a fiberoptic cable. As another example, communications interface 470 may be alocal area network (LAN) card to provide a data communication connectionto a compatible LAN, such as Ethernet. Wireless links may also beimplemented. For wireless links, the communications interface 470 sendsand receives electrical, acoustic or electromagnetic signals, includinginfrared and optical signals, that carry information streams, such asdigital data. Such signals are examples of carrier waves.

The term computer-readable medium is used herein to refer to any mediumthat participates in providing information to processor 402, includinginstructions for execution. Such a medium may take many forms,including, but not limited to, non-volatile media, volatile media andtransmission media. Non-volatile media include, for example, optical ormagnetic disks, such as storage device 408. Volatile media include, forexample, dynamic memory 404. Transmission media include, for example,coaxial cables, copper wire, fiber optic cables, and waves that travelthrough space without wires or cables, such as acoustic waves andelectromagnetic waves, including radio, optical and infrared waves.Signals that are transmitted over transmission media are herein calledcarrier waves.

Common forms of computer-readable media include, for example, a floppydisk, a flexible disk, a hard disk, a magnetic tape, or any othermagnetic medium, a compact disk ROM (CD-ROM), a digital video disk (DVD)or any other optical medium, punch cards, paper tape, or any otherphysical medium with patterns of holes, a RAM, a programmable ROM(PROM), an erasable PROM (EPROM), a FLASH-EPROM, or any other memorychip or cartridge, a carrier wave, or any other medium from which acomputer can read.

Network link 478 typically provides information communication throughone or more networks to other devices that use or process theinformation. For example, network link 478 may provide a connectionthrough local network 480 to a host computer 482 or to equipment 484operated by an Internet Service Provider (ISP). ISP equipment 484 inturn provides data communication services through the public, world-widepacket-switching communication network of networks now commonly referredto as the Internet 490. A computer called a server 492 connected to theInternet provides a service in response to information received over theInternet. For example, server 492 provides information representingvideo data for presentation at display 414.

The invention is related to the use of computer system 400 forimplementing the techniques described herein. According to oneembodiment of the invention, those techniques are performed by computersystem 400 in response to processor 402 executing one or more sequencesof one or more instructions contained in memory 404. Such instructions,also called software and program code, may be read into memory 404 fromanother computer-readable medium such as storage device 408. Executionof the sequences of instructions contained in memory 404 causesprocessor 402 to perform the method steps described herein. Inalternative embodiments, hardware, such as application specificintegrated circuit 420, may be used in place of or in combination withsoftware to implement the invention. Thus, embodiments of the inventionare not limited to any specific combination of hardware and software.

The signals transmitted over network link 478 and other networks throughcommunications interface 470, which carry information to and fromcomputer system 400, are exemplary forms of carrier waves. Computersystem 400 can send and receive information, including program code,through the networks 480, 490 among others, through network link 478 andcommunications interface 470. In an example using the Internet 490, aserver 492 transmits program code for a particular application,requested by a message sent from computer 400, through Internet 490, ISPequipment 484, local network 480 and communications interface 470. Thereceived code may be executed by processor 402 as it is received, or maybe stored in storage device 408 or other non-volatile storage for laterexecution, or both. In this manner, computer system 400 may obtainapplication program code in the form of a carrier wave.

Various forms of computer readable media may be involved in carrying oneor more sequence of instructions or data or both to processor 402 forexecution. For example, instructions and data may initially be carriedon a magnetic disk of a remote computer such as host 482. The remotecomputer loads the instructions and data into its dynamic memory andsends the instructions and data over a telephone line using a modem. Amodem local to the computer system 400 receives the instructions anddata on a telephone line and uses an infra-red transmitter to convertthe instructions and data to an infra-red signal, a carrier wave servingas the network link 478. An infrared detector serving as communicationsinterface 470 receives the instructions and data carried in the infraredsignal and places information representing the instructions and dataonto bus 410. Bus 410 carries the information to memory 404 from whichprocessor 402 retrieves and executes the instructions using some of thedata sent with the instructions. The instructions and data received inmemory 404 may optionally be stored on storage device 408, either beforeor after execution by the processor 402.

4. Extensions and Alternatives

In the foregoing specification, the invention has been described withreference to specific embodiments thereof. It will, however, be evidentthat various modifications and changes may be made thereto withoutdeparting from the broader spirit and scope of the invention. Thespecification and drawings are, accordingly, to be regarded in anillustrative rather than a restrictive sense.

1. A method comprising: determining an analytical solution expressing atemporal change in probability that a component of a system is in aparticular state of four possible states at a particular time after aninitial time based on four explicitly recited initial probabilitiescorresponding to the four states and twelve explicitly recited rateconstants for switching from one state of the four states to a differentstate of the four states, wherein any rate constant or initialprobability may take on a value of zero; determining a non zero valueassociated with an external factor applied to the component for each ofone or more of the twelve rate constants; determining a non zero valuefor each of one or more of the four initial probabilities; anddetermining, on a programmed processor, a temporal change in aprobability that the component is in a certain state of the four statesbased on the values for the twelve rate constants and on the values forthe four initial probabilities and on an elapsed time and on theanalytical solution.
 2. A method as recited in claim 1, wherein thecomponent is a gated ion channel, the system is a membrane of a cell,and the external factor is at least one of an applied voltage and aconcentration of a ligand.
 3. A method as recited in claim 1, furthercomprising determining a computed effect of the external factor on thesystem based, at least in part, on the temporal change in theprobability that the component is in the certain state.
 4. A method asrecited in claim 3, further comprising: measuring the system at theelapsed time; and comparing a measured change in the system to thecomputed effect of the external factor.
 5. A method as recited in claim4, wherein measuring the system at the elapsed time comprises measuringan electric current passing through a membrane of a cell.
 6. A method asrecited in claim 4, further comprising revising a value associated withthe external factor for one or more of the twelve rate constants basedon comparing the measured change in the system to the computed effect ofthe external factor.
 7. A non-transitory computer-readable mediumcarrying one or more sequences of instructions, wherein execution of theone or more sequences of instructions by one or more processors causesan apparatus to: determine data that indicates an analytical solutionexpressing a temporal change in probability that a component of a systemis in a particular state of four possible states at a particular timeafter an initial time based on four explicitly recited initialprobabilities corresponding to the four states and twelve explicitlyrecited rate constants for switching from one state of the four statesto a different state of the four states; determine a non zero valueassociated with an external factor applied to the component for each ofone or more of the twelve rate constants; determine a non zero value foreach of one or more of the four initial probabilities; and determine atemporal change in a probability that the component is in a certainstate of the four states based on the values for the twelve rateconstants and on the values for the four initial probabilities and on anelapsed time and on the analytical solution.
 8. A non-transitorycomputer-readable medium as recited in claim 7, wherein the component isa gated ion channel, the system is a membrane of a cell, and theexternal factor is at least one of an applied voltage and aconcentration of a ligand.
 9. A non-transitory computer-readable mediumas recited in claim 7, wherein the apparatus is further caused todetermine a computed effect of the external factor on the system based,at least in part, on the temporal change in the probability that thecomponent is in the certain state.
 10. A non-transitorycomputer-readable medium as recited in claim 9, wherein the apparatus isfurther caused to: measure the system after the elapsed time; andcompare a measured change in the system to the computed effect of theexternal factor.
 11. A non-transitory computer-readable medium asrecited in claim 10, wherein measuring the system after the elapsed timecomprises measuring an electric current passing through a membrane of acell.
 12. A non-transitory computer-readable medium as recited in claim10, wherein the apparatus is further caused to revise a value associatedwith the external factor for one or more of the twelve rate constantsbased on comparing the measured change in the system to the computedeffect of the external factor.
 13. An apparatus comprising: one or moreprocessors; and a computer-readable medium carrying one or moresequences of instructions, wherein execution of the one or moresequences of instructions by the one or more processors causes theapparatus to: determine data that indicates an analytical solutionexpressing a temporal change in probability that a component of a systemis in a particular state of four possible states at a particular timeafter an initial time based on four explicitly recited initialprobabilities corresponding to the four states and twelve explicitlyrecited rate constants for switching from one state of the four statesto a different state of the four states; determine a non zero valueassociated with an external factor applied to the component for each ofone or more of the twelve rate constants; determine a non zero value foreach of one or more of the four initial probabilities; and determine atemporal change in a probability that the component is in a certainstate of the four states based on the values for the twelve rateconstants and on the values for the four initial probabilities and on anelapsed time and on the analytical solution.
 14. An apparatus as recitedin claim 13, wherein the component is a gated ion channel, the system isa membrane of a cell, and the external factor is at least one of anapplied voltage and a concentration of a ligand.
 15. An apparatus asrecited in claim 13, wherein the apparatus is further caused todetermine a computed effect of the external factor on the system based,at least in part, on the temporal change in the probability that thecomponent is in the certain state.
 16. An apparatus as recited in claim15, wherein the apparatus is further caused to: measure the system afterthe elapsed time; and compare a measured change in the system to thecomputed effect of the external factor.
 17. An apparatus as recited inclaim 16, wherein measuring the system after the elapsed time comprisesmeasuring an electric current passing through a membrane of a cell. 18.An apparatus as recited in claim 16, wherein the apparatus is furthercaused to revise a value associated with the external factor for one ormore of the twelve rate constants based on comparing the measured changein the system to the computed effect of the external factor.